Supervised Learning

Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset, meaning the input data has a corresponding output label. The goal of supervised learning is to learn a mapping function that can predict the output label for new input data. This is achieved by minimizing the error between the predicted output and the true output.

Examples

• Image classification: The input data is an image and the output label is a description of what is in the image, such as 'cat' or 'dog'.
• Spam detection in emails: The input data is the content of the email, and the output label is whether the email is spam or not.

Types of Supervised Learning Algorithms

• Regression: Used when the output label is a continuous value, such as predicting the price of a house.
• Classification: Used when the output label is a discrete value, such as predicting if an email is spam or not.

Applications

Supervised learning has many applications in various industries. For example, in healthcare, it can be used to predict the risk of a patient developing a certain disease. In finance, it can be used to predict stock prices. In transportation, it can be used to predict traffic patterns and optimize route planning.

All courses were automatically generated using OpenAI's GPT-3. Your feedback helps us improve as we cannot manually review every course. Thank you!